mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 12:42:51 +08:00
538 lines
14 KiB
Python
538 lines
14 KiB
Python
import tiktoken
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
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from llmtuner.extras.logging import get_logger
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if TYPE_CHECKING:
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from transformers import PreTrainedTokenizer
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logger = get_logger(__name__)
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@dataclass
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class Template:
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prefix: List[Union[str, Dict[str, str]]]
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prompt: List[Union[str, Dict[str, str]]]
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sep: List[Union[str, Dict[str, str]]]
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stop_words: List[str]
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use_history: bool
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def encode_oneturn(
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self,
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tokenizer: "PreTrainedTokenizer",
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query: str,
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resp: str,
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history: Optional[List[Tuple[str, str]]] = None,
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prefix: Optional[str] = None
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) -> Tuple[List[int], List[int]]:
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r"""
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Returns a single pair of token ids representing prompt and response respectively.
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"""
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prefix, history = self._format(query, resp, history, prefix)
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encoded_pairs = self._encode(tokenizer, prefix, history)
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prompt_ids = []
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for query_ids, resp_ids in encoded_pairs[:-1]:
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prompt_ids = prompt_ids + query_ids + resp_ids
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prompt_ids = prompt_ids + encoded_pairs[-1][0]
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return prompt_ids, encoded_pairs[-1][1]
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def encode_multiturn(
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self,
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tokenizer: "PreTrainedTokenizer",
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query: str,
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resp: str,
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history: Optional[List[Tuple[str, str]]] = None,
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prefix: Optional[str] = None
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) -> List[Tuple[List[int], List[int]]]:
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r"""
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Returns multiple pairs of token ids representing prompts and responses respectively.
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"""
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prefix, history = self._format(query, resp, history, prefix)
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encoded_pairs = self._encode(tokenizer, prefix, history)
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return encoded_pairs
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def _format(
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self,
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query: str,
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resp: str,
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history: Optional[List[Tuple[str, str]]] = None,
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prefix: Optional[str] = None
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) -> Tuple[List[Union[str, Dict[str, str]]], List[Tuple[str, str]]]:
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r"""
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Aligns inputs to the standard format.
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"""
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prefix = [prefix] if prefix else self.prefix # use prefix if provided
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history = history if (history and self.use_history) else []
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history = history + [(query, resp)]
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return prefix, history
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def _get_special_ids(
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self,
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tokenizer: "PreTrainedTokenizer"
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) -> Tuple[List[int], List[int]]:
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if tokenizer.bos_token_id:
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bos_ids = [tokenizer.bos_token_id]
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else:
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bos_ids = [] # bos token is optional
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if tokenizer.eos_token_id:
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eos_ids = [tokenizer.eos_token_id]
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else:
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raise ValueError("EOS token is required.")
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return bos_ids, eos_ids
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def _encode(
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self,
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tokenizer: "PreTrainedTokenizer",
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prefix: List[Union[str, Dict[str, str]]],
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history: List[Tuple[str, str]]
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) -> List[Tuple[List[int], List[int]]]:
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r"""
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Encodes formatted inputs to pairs of token ids.
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Turn 0: bos + prefix + sep + query resp + eos
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Turn t: sep + bos + query resp + eos
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"""
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bos_ids, eos_ids = self._get_special_ids(tokenizer)
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sep_ids = self._convert_inputs_to_ids(tokenizer, context=self.sep)
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encoded_pairs = []
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for turn_idx, (query, resp) in enumerate(history):
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if turn_idx == 0:
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if prefix: # has prefix
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prefix_ids = bos_ids + self._convert_inputs_to_ids(tokenizer, context=prefix) + sep_ids
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else:
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prefix_ids = bos_ids
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else:
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prefix_ids = sep_ids + bos_ids
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query_ids = self._convert_inputs_to_ids(tokenizer, context=self.prompt, query=query, idx=str(turn_idx))
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resp_ids = self._convert_inputs_to_ids(tokenizer, context=[resp])
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encoded_pairs.append((prefix_ids + query_ids, resp_ids + eos_ids))
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return encoded_pairs
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def _convert_inputs_to_ids(
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self,
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tokenizer: "PreTrainedTokenizer",
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context: List[Union[str, Dict[str, str]]],
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query: Optional[str] = "",
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idx: Optional[str] = ""
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) -> List[int]:
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r"""
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Converts context to token ids.
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"""
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if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding): # for tiktoken tokenizer (Qwen)
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kwargs = dict(allowed_special="all")
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else:
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kwargs = dict(add_special_tokens=False)
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token_ids = []
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for elem in context:
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if isinstance(elem, str):
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elem = elem.replace("{{query}}", query, 1)
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elem = elem.replace("{{idx}}", idx, 1)
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token_ids = token_ids + tokenizer.encode(elem, **kwargs)
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elif isinstance(elem, dict):
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token_ids = token_ids + [tokenizer.convert_tokens_to_ids(elem.get("token"))]
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else:
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raise NotImplementedError
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return token_ids
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@dataclass
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class Llama2Template(Template):
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def _encode(
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self,
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tokenizer: "PreTrainedTokenizer",
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prefix: List[Union[str, Dict[str, str]]],
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history: List[Tuple[str, str]]
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) -> List[Tuple[List[int], List[int]]]:
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r"""
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Encodes formatted inputs to pairs of token ids.
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Turn 0: bos + prefix + query resp + eos
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Turn t: bos + query resp + eos
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"""
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bos_ids, eos_ids = self._get_special_ids(tokenizer)
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encoded_pairs = []
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assert isinstance(prefix[0], str), "LLaMA-2 template only accepts list containing a single string."
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for turn_idx, (query, resp) in enumerate(history):
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if turn_idx == 0: # llama2 template has not sep_ids
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query = prefix[0] + query
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query_ids = self._convert_inputs_to_ids(tokenizer, context=self.prompt, query=query)
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resp_ids = self._convert_inputs_to_ids(tokenizer, context=[resp])
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encoded_pairs.append((bos_ids + query_ids, resp_ids + eos_ids))
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return encoded_pairs
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templates: Dict[str, Template] = {}
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def register_template(
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name: str,
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prefix: List[Union[str, Dict[str, str]]],
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prompt: List[Union[str, Dict[str, str]]],
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sep: List[Union[str, Dict[str, str]]],
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stop_words: List[str],
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use_history: bool
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) -> None:
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template_class = Llama2Template if "llama2" in name else Template
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templates[name] = template_class(
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prefix=prefix,
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prompt=prompt,
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sep=sep,
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stop_words=stop_words,
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use_history=use_history
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)
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def get_template_and_fix_tokenizer(
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name: str,
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tokenizer: "PreTrainedTokenizer"
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) -> Template:
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template = templates.get(name, None)
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assert template is not None, "Template {} does not exist.".format(name)
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if len(template.stop_words): # inplace method
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tokenizer.eos_token = template.stop_words[0]
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logger.info("Replace eos token: {}".format(tokenizer.eos_token))
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if tokenizer.eos_token_id is None:
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tokenizer.eos_token = "<|endoftext|>"
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logger.info("Add eos token: {}".format(tokenizer.eos_token))
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if tokenizer.pad_token_id is None:
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tokenizer.pad_token = tokenizer.eos_token
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logger.info("Add pad token: {}".format(tokenizer.pad_token))
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tokenizer.add_special_tokens(dict(additional_special_tokens=template.stop_words))
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return template
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r"""
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Supports language model inference without histories.
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"""
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register_template(
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name="vanilla",
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prefix=[],
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prompt=[
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"{{query}}"
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],
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sep=[],
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stop_words=[],
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use_history=False
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)
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r"""
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Default template.
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"""
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register_template(
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name="default",
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prefix=[
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"A chat between a curious user and an artificial intelligence assistant. "
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"The assistant gives helpful, detailed, and polite answers to the user's questions."
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],
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prompt=[
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"Human: {{query}}\nAssistant: "
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],
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sep=[
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"\n"
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],
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stop_words=[],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
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https://huggingface.co/meta-llama/Llama-2-13b-chat-hf
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https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
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"""
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register_template(
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name="llama2",
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prefix=[
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"<<SYS>>\nYou are a helpful, respectful and honest assistant. "
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"Always answer as helpfully as possible, while being safe. "
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"Your answers should not include any harmful, unethical, "
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"racist, sexist, toxic, dangerous, or illegal content. "
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"Please ensure that your responses are socially unbiased and positive in nature.\n"
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"If a question does not make any sense, or is not factually coherent, "
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"explain why instead of answering something not correct. "
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"If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
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],
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prompt=[
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"[INST] {{query}} [/INST] "
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],
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sep=[],
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stop_words=[],
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use_history=True
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)
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r"""
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Supports: https://github.com/ymcui/Chinese-LLaMA-Alpaca-2
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"""
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register_template(
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name="llama2_zh",
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prefix=[
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"<<SYS>>\nYou are a helpful assistant. 你是一个乐于助人的助手。\n<</SYS>>\n\n"
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],
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prompt=[
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"[INST] {{query}} [/INST] "
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],
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sep=[],
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stop_words=[],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/tatsu-lab/alpaca-7b-wdiff
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https://github.com/ymcui/Chinese-LLaMA-Alpaca
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"""
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register_template(
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name="alpaca",
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prefix=[
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"Below is an instruction that describes a task. "
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"Write a response that appropriately completes the request."
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],
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prompt=[
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"### Instruction:\n{{query}}\n\n### Response:\n"
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],
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sep=[
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"\n\n"
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],
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stop_words=[],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/lmsys/vicuna-7b-delta-v1.1
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https://huggingface.co/lmsys/vicuna-13b-delta-v1.1
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"""
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register_template(
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name="vicuna",
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prefix=[
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"A chat between a curious user and an artificial intelligence assistant. "
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"The assistant gives helpful, detailed, and polite answers to the user's questions."
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],
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prompt=[
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"USER: {{query}} ASSISTANT: "
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],
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sep=[],
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stop_words=[],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/BelleGroup/BELLE-LLaMA-EXT-13B
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"""
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register_template(
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name="belle",
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prefix=[],
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prompt=[
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"Human: {{query}}\n\nBelle: "
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],
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sep=[
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"\n\n"
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],
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stop_words=[],
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use_history=True
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)
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r"""
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Supports: https://github.com/CVI-SZU/Linly
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"""
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register_template(
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name="linly",
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prefix=[],
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prompt=[
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"User: {{query}}\nBot: "
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],
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sep=[
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"\n"
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],
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stop_words=[],
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use_history=True
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)
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r"""
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Supports: https://github.com/Neutralzz/BiLLa
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"""
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register_template(
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name="billa",
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prefix=[],
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prompt=[
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"Human: {{query}}\nAssistant: "
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],
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sep=[
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"\n"
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],
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stop_words=[],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1
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"""
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register_template(
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name="ziya",
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prefix=[],
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prompt=[
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{"token": "<human>"},
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":{{query}}\n",
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{"token": "<bot>"},
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":"
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],
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sep=[
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"\n"
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],
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stop_words=[],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/qhduan/aquilachat-7b
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"""
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register_template(
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name="aquila",
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prefix=[
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"A chat between a curious human and an artificial intelligence assistant. "
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"The assistant gives helpful, detailed, and polite answers to the human's questions."
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],
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prompt=[
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"Human: {{query}}###Assistant: "
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],
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sep=[
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"###"
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],
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stop_words=[],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/internlm/internlm-chat-7b
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"""
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register_template(
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name="intern",
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prefix=[],
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prompt=[
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"<|User|>:{{query}}",
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{"token": "<eoh>"},
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"\n<|Bot|>:"
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],
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sep=[
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"\n"
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],
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stop_words=[
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"<eoa>"
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],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat
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"""
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register_template(
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name="baichuan",
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prefix=[
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{"token": "<reserved_102>"} # user token (a little difference in the first turn)
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],
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prompt=[
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"{{query}}",
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{"token": "<reserved_103>"} # assistant token
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],
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sep=[],
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stop_words=[
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"<reserved_102>" # user token
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],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/HuggingFaceH4/starchat-alpha
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https://huggingface.co/HuggingFaceH4/starchat-beta
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"""
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register_template(
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name="starchat",
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prefix=[
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{"token": "<|system|>"},
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"\n",
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{"token": "<|end|>"}
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],
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prompt=[
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{"token": "<|user|>"},
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"\n{{query}}",
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{"token": "<|end|>"},
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"\n",
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{"token": "<|assistant|>"}
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],
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sep=[
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"\n"
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],
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stop_words=[
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"<|end|>"
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],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/Qwen/Qwen-7B-Chat
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"""
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register_template(
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name="chatml",
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prefix=[
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{"token": "<|im_start|>"},
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"system\nYou are a helpful assistant.",
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{"token": "<|im_end|>"}
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],
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prompt=[
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{"token": "<|im_start|>"},
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"user\n{{query}}",
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{"token": "<|im_end|>"},
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"\n",
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{"token": "<|im_start|>"},
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"assistant\n"
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],
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sep=[
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"\n"
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],
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stop_words=[
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"<|im_end|>"
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],
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use_history=True
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)
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r"""
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Supports: https://huggingface.co/THUDM/chatglm2-6b
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"""
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register_template(
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name="chatglm2",
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prefix=[
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{"token": "[gMASK]"},
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{"token": "sop"}
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],
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prompt=[
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"[Round {{idx}}]\n\n问：{{query}}\n\n答："
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],
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sep=[
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"\n\n"
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],
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stop_words=[],
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use_history=True
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)
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